{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align = 'center'> Learning To See </h1>\n",
    "\n",
    "<h2 align = 'center'> 第二部分:规则的规则的规则 </h2>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 启用 pylab 模式，使得可以在 Jupyter Notebook 中实时显示图表\n",
    "%matplotlib inline\n",
    "\n",
    "# 导入 pickle 库，并使用别名 cPickle\n",
    "import pickle as cPickle\n",
    "\n",
    "# 定义 pickle 文件的文件名\n",
    "pickleFileName = 'data/fingerDataSet' + '.pickle'\n",
    "\n",
    "# 以二进制读模式打开 pickle 文件，使用 latin1 编码\n",
    "with open(pickleFileName, 'rb') as pickleFile:\n",
    "    # 使用 pickle.load() 从 pickle 文件中加载数据\n",
    "    data = cPickle.load(pickleFile, encoding='latin1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"data\"是一个python字典列表，其中包含大量预先计算的信息，随着本系列的进展，我们会发现这些信息很有用:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['box', 'boxHeight', 'handPoints', 'trackingIndices', 'allFingerPoints', 'boxWidth', 'image', 'boxEdges', 'croppedImage', 'numFingers', 'numPointsInBox', 'handEdges', 'indexFingerPoints', 'picCount', 'image1bit'])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0].keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 看几个例子吧"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个大小为12x8的图表对象\n",
    "fig = plt.figure(0, (12, 8))\n",
    "\n",
    "# 定义示例图像的索引列表\n",
    "exampleIndices = [7, 30, 46]\n",
    "\n",
    "# 遍历示例图像索引列表\n",
    "for i, exampleIndex in enumerate(exampleIndices):\n",
    "    # 将图表分成1行3列，选择当前子图\n",
    "    plt.subplot(1, 3, i + 1)\n",
    "    \n",
    "    # 使用 imshow 函数显示图像，灰度颜色映射 'gray'，插值方式 'none'\n",
    "    plt.imshow(data[exampleIndex]['croppedImage'], cmap='gray', interpolation='none')\n",
    "    \n",
    "    # 关闭坐标轴\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 从8位降采样到1位"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hCwAAQJqwBQAAIM1zPwAAwCnWngKCZ5jYAgAAkCZsAQAASBO2AAAApAlbAAAA0oQtAAAAacIWAACANGELAABAmrAFAAAgTdgCAACQJmwBAABIE7YAAACkCVsAAADShC0AAABpwhYAAIA0YQsAAECasAUAACBN2AIAAJD2N0zqX524ihseAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个大小为12x8的图表对象\n",
    "fig = plt.figure(0, (12, 8))\n",
    "\n",
    "# 定义示例图像的索引列表\n",
    "exampleIndices = [7, 30, 46]\n",
    "\n",
    "# 遍历示例图像索引列表\n",
    "for i, exampleIndex in enumerate(exampleIndices):\n",
    "    # 将图表分成1行3列，选择当前子图\n",
    "    plt.subplot(1, 3, i + 1)\n",
    "    \n",
    "    # 使用 imshow 函数显示图像，灰度颜色映射 'gray'，插值方式 'none'\n",
    "    # 将像素值小于92的部分设置为白色，大于等于92的部分设置为黑色\n",
    "    plt.imshow(data[exampleIndex]['croppedImage'] < 92, cmap='gray', interpolation='none')\n",
    "    \n",
    "    # 关闭坐标轴\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为方便起见，1位图像也被预先计算过:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2aa4f040c08>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用 imshow 函数显示图像的一部分\n",
    "#这一部分从data中获取特定索引0处的图像数据，然后使用切片操作,从图像中选择一个特定区域。\n",
    "plt.imshow(data[0]['image1bit'][data[0]['boxEdges'][2]:data[0]['boxEdges'][3], \n",
    "                                data[0]['boxEdges'][0]:data[0]['boxEdges'][1]],\n",
    "           cmap='Greys', interpolation='none')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 从图像中提取9x9的示例网格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 导入自定义的支持函数库\n",
    "from supportFunctions import *\n",
    "\n",
    "# 从数据集中获取指定索引的示例图像\n",
    "examples = [data[index] for index in exampleIndices]\n",
    "\n",
    "# 从示例图像中提取特征和标签，使用 'image1bit' 作为特征，dist=4 表示样本之间的间隔\n",
    "X, y = extractExamplesFromList(examples, whichImage='image1bit', dist=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9x9个例子被展开成更大的矩阵x, y包含标签，0表示不是手指，1表示有手指"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7867, 81) (7867,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们的9x9网格现在只包含1和0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x2aa503bc848>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 从特征矩阵 X 中选择一个示例，并将其重塑为9x9的矩阵\n",
    "exampleToShow = X[421, :].reshape(9, 9)\n",
    "\n",
    "# 创建一个大小为4x4的图表对象\n",
    "fig = plt.figure(0, (4, 4))\n",
    "\n",
    "# 使用 pcolor 函数绘制示例图像，颜色映射 'Greys'，边框线宽度0.5，边框颜色为黑色\n",
    "# vmin 和 vmax 分别设置颜色映射的最小和最大值\n",
    "plt.pcolor(flipud(exampleToShow), cmap='Greys', linewidth=0.5, color='k', vmin=0, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 1 1 0 0]\n",
      " [0 0 0 0 1 1 1 1 0]\n",
      " [0 0 0 0 1 1 1 1 0]\n",
      " [0 0 0 0 1 1 1 1 0]\n",
      " [0 0 0 1 1 1 1 1 0]\n",
      " [0 0 0 1 1 1 1 1 0]]\n"
     ]
    }
   ],
   "source": [
    "# 打印示例图像矩阵，将其元素类型转换为整数\n",
    "print(exampleToShow.astype('int'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 让我们制定一个(简单的)规则来对手指进行分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "rule1 = np.array(([[0, 0, 1, 1, 1, 1, 1, 0, 0],\n",
    "                   [0, 0, 1, 1, 1, 1, 1, 0, 0],\n",
    "                   [0, 0, 1, 1, 1, 1, 1, 0, 0],\n",
    "                   [0, 0, 1, 1, 1, 1, 1, 0, 0],\n",
    "                   [0, 0, 1, 1, 1, 1, 1, 0, 0],\n",
    "                   [0, 0, 1, 1, 1, 1, 1, 0, 0],\n",
    "                   [0, 0, 1, 1, 1, 1, 1, 0, 0],\n",
    "                   [0, 0, 1, 1, 1, 1, 1, 0, 0],\n",
    "                   [0, 0, 1, 1, 1, 1, 1, 0, 0]])) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x2aa4e8a3848>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个大小为4x4的图表对象\n",
    "fig = plt.figure(0, (4, 4))\n",
    "# 使用 pcolor 函数绘制规则图像，颜色映射 'Greys'，边框线宽度1，边框颜色为黑色\n",
    "# vmin 和 vmax 分别设置颜色映射的最小和最大值\n",
    "plt.pcolor(flipud(rule1), cmap='Greys', linewidth=1, color='k', vmin=0, vmax=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在数据中搜索与新规则相匹配的内容:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 737  782  827  872 3415 3453 3491 5957 5997]\n"
     ]
    }
   ],
   "source": [
    "# 计算特征矩阵 X 与规则图像 rule1 的差异\n",
    "difference = X - rule1.ravel()\n",
    "\n",
    "# 使用 np.where 找到差异矩阵中所有行的索引，其中所有元素均为零\n",
    "zero_difference_rows = np.where(~difference.any(axis=1))[0]\n",
    "\n",
    "# 打印索引值\n",
    "print(zero_difference_rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "找到了一些火柴!让我们想象一下这些吸盘。我们必须一次浏览一张图片:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个大小为12x6的图表对象\n",
    "fig = plt.figure(0, (12, 6))\n",
    "\n",
    "# 调用自定义函数 showMatches 显示匹配结果\n",
    "showMatches(rules=[rule1], exampleIndices=exampleIndices, data=data, fig=fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过移动我们的手指图案来创造更多规则"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "ruleToSample = np.array(([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "                          [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "                          [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "                          [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "                          [ 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],\n",
    "                          [ 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n",
    "                          [ 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n",
    "                          [ 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n",
    "                          [ 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n",
    "                          [ 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n",
    "                          [ 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0],\n",
    "                          [ 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0], \n",
    "                          [ 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0], \n",
    "                          [ 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 创建一个空的规则列表\n",
    "rules = []\n",
    "\n",
    "# 使用双重循环生成规则\n",
    "for i in range(6):\n",
    "    for j in range(5):\n",
    "        # 将从规则模板中切片得到的子矩阵添加到规则列表中\n",
    "        rules.append(ruleToSample[i:i+9, j:j+9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1200 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个大小为10x12的图表对象\n",
    "fig = plt.figure(0, (10, 12))\n",
    "\n",
    "# 遍历规则列表，使用 enumerate 获取规则和索引\n",
    "for i, rule in enumerate(rules):\n",
    "    # 将图表分成6行5列，选择当前子图\n",
    "    plt.subplot(6, 5, i + 1)\n",
    "    \n",
    "    # 使用 pcolor 函数绘制规则图像，颜色映射 'Greys'，边框线宽度1，边框颜色为黑色\n",
    "    # vmin 和 vmax 分别设置颜色映射的最小和最大值\n",
    "    p = plt.pcolor(flipud(rule), cmap='Greys', linewidth=1, color='k', vmin=0, vmax=1)\n",
    "    \n",
    "    # 关闭坐标轴\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们现在有30条规则了，让我们试试吧!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个大小为12x6的图表对象\n",
    "fig = plt.figure(0, (12, 6))\n",
    "\n",
    "# 调用自定义函数 showMatches 显示多个规则在示例图像上的匹配结果\n",
    "showMatches(rules=rules, exampleIndices=exampleIndices, data=data, fig=fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "好的，更多的匹配！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 我们做得怎么样?\n",
    "- 让我们量化我们的表现\n",
    "- 要做到这一点，我们需要正确的标签。\n",
    "- 我们可以用手标记像素:)，但我们会使用跳跃运动软件来节省时间。有关详细信息，请参阅预处理代码和跳跃运动代码。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 来自Leap Motion软件的手指标签\n",
    "- 这些并不完美，但我们会假设它们足够接近，并让这些标签成为我们的“基本事实”。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个大小为12x6的图表对象\n",
    "fig = plt.figure(0, (12, 6))\n",
    "\n",
    "# 遍历示例图像索引列表的前三个示例\n",
    "for i in range(3):\n",
    "    # 将图表分成1行3列，并选择当前子图\n",
    "    fig.add_subplot(1, 3, i + 1)\n",
    "    \n",
    "    # 从数据集中获取示例图像的字典\n",
    "    imageDict = data[exampleIndices[i]]\n",
    "    \n",
    "    # 从示例图像中提取特征和标签\n",
    "    X1, y1 = extractFeatures(imageDict, whichImage='image1bit', dist=4)    \n",
    "    yImage = y1.reshape(imageDict['boxHeight'], imageDict['boxWidth'])\n",
    "    \n",
    "    # 生成灰度图像\n",
    "    im = makeGrayScale(imageDict)\n",
    "    \n",
    "    # 根据标签信息对图像进行着色\n",
    "    im[:, :, 0][yImage == 1] = 0\n",
    "    im[:, :, 1][yImage == 1] = 0.5\n",
    "    im[:, :, 2][yImage == 1] = 1\n",
    "    \n",
    "    # 使用 imshow 函数显示图像\n",
    "    plt.imshow(im)\n",
    "    \n",
    "    # 设置子图标题\n",
    "    plt.title('Number of finger pixels = ' + str(sum(y1 == 1)), fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 计算混淆矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  48  447]\n",
      " [   7 7365]]\n"
     ]
    }
   ],
   "source": [
    "# 在规则列表中搜索匹配项\n",
    "matchingIndices = np.array([], dtype='int')\n",
    "for rule in rules:\n",
    "    difference = X - rule.ravel()\n",
    "    mI = np.where(~difference.any(axis=1))[0]\n",
    "    matchingIndices = np.concatenate((matchingIndices, mI))\n",
    "\n",
    "# 创建 yHat 向量，指示规则预测为手指的像素\n",
    "yHat = np.zeros(X.shape[0])\n",
    "yHat[matchingIndices] = 1\n",
    "\n",
    "# 计算假阴性、假阳性、真阳性和真阴性的数量\n",
    "FN = np.sum(np.logical_and(y == 1, yHat == 0))\n",
    "FP = np.sum(np.logical_and(y == 0, yHat == 1))\n",
    "TP = np.sum(np.logical_and(y == 1, yHat == 1))\n",
    "TN = np.sum(np.logical_and(y == 0, yHat == 0))\n",
    "\n",
    "# 构建混淆矩阵\n",
    "confusionMatrix = np.array([[TP, FN], [FP, TN]])\n",
    "\n",
    "print(confusionMatrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "性能不是很好!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 多色图大表\n",
    "- 稍后取出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[  48  447]\n",
      " [   7 7365]]\n",
      "Recall (TPR) = 0.097 (Portion of fingers that we \"caught\")\n",
      "Precision (PPV) = 0.873(Portion of predicted finger pixels that were actually finger pixels)\n",
      "Accuracy = 0.942\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个大小为14x8的图表对象\n",
    "fig = plt.figure(0, (14, 8))\n",
    "\n",
    "# 调用自定义函数 testRules 进行规则测试\n",
    "testRules(rules, exampleIndices, data, fig, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 返回只使用3张图像作为训练数据：\n",
    "# 训练示例图像的索引列表\n",
    "trainingExampleIndices = [7, 30, 46]\n",
    "\n",
    "# 从数据集中获取训练示例图像的列表\n",
    "trainingExamples = [data[index] for index in trainingExampleIndices]\n",
    "\n",
    "# 从训练示例图像中提取特征和标签，使用 'image1bit' 作为特征，dist=4 表示样本之间的间隔\n",
    "trainX, trainY = extractExamplesFromList(trainingExamples, whichImage='image1bit', dist=4)\n",
    "\n",
    "# 选择两张图像作为测试数据：\n",
    "# 测试示例图像的索引列表\n",
    "testingExampleIndices = [34, 45]\n",
    "\n",
    "# 从数据集中获取测试示例图像的列表\n",
    "testingExamples = [data[index] for index in testingExampleIndices]\n",
    "\n",
    "# 从测试示例图像中提取特征和标签，使用 'image1bit' 作为特征，dist=4 表示样本之间的间隔\n",
    "testX, testY = extractExamplesFromList(testingExamples, whichImage='image1bit', dist=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[  55  341]\n",
      " [  15 4890]]\n",
      "Recall (TPR) = 0.139 (Portion of fingers that we \"caught\")\n",
      "Precision (PPV) = 0.786(Portion of predicted finger pixels that were actually finger pixels)\n",
      "Accuracy = 0.933\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个大小为14x8的图表对象\n",
    "fig = plt.figure(0, (14, 8))\n",
    "\n",
    "# 调用自定义函数 testRules 进行规则测试（在测试数据上）\n",
    "testRules(rules, testingExampleIndices, data, fig, testX, testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "rules = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[   0  495]\n",
      " [   0 7372]]\n",
      "Recall (TPR) = 0.0 (Portion of fingers that we \"caught\")\n",
      "Precision (PPV) = 0(Portion of predicted finger pixels that were actually finger pixels)\n",
      "Accuracy = 0.937\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个大小为14x8的图表对象\n",
    "fig = plt.figure(0, (14, 8))\n",
    "\n",
    "# 调用自定义函数 testRules 进行规则测试（在训练数据上）\n",
    "testRules(rules, trainingExampleIndices, data, fig, trainX, trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[   0  495]\n",
      " [   0 7372]]\n",
      "Recall (TPR) = 0.0 (Portion of fingers that we \"caught\")\n",
      "Precision (PPV) = 0(Portion of predicted finger pixels that were actually finger pixels)\n",
      "Accuracy = 0.937\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个大小为14x8的图表对象\n",
    "fig = plt.figure(0, (14, 8))\n",
    "\n",
    "# 调用自定义函数 testRules 进行规则测试（在训练数据上）\n",
    "testRules(rules, trainingExampleIndices, data, fig, trainX, trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "depth-camera",
   "language": "python",
   "name": "depth-camera"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.16"
  },
  "latex_envs": {
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 0
  },
  "toc": {
   "toc_cell": false,
   "toc_number_sections": false,
   "toc_section_display": "block",
   "toc_threshold": 6,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
